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Orthogonal time frequency space (OTFS) modulation is a newly proposed modulation technique for providing a solution to high mobility doubly dispersive channel problems. In several recent research works, it is shown that OTFS has better performance over the existing conventional multicarrier modulations. OTFS modulate information symbols in a two-dimensional (2D) delay-Doppler domain rather than in time frequency domain, which can exploit the full channel diversity over time and frequency. This unique ability of OTFS can provide to design an advanced signal detection method. In this letter, we present a deep learning-based signal detection for OTFS systems. Since the input-output relation of OTFS is in 2D delay-Doppler domain, we propose a two-dimensional convolutional neural network (2D-CNN) based detector. We also employ data augmentation technique based on the widely used message-passing (MP) algorithm to improve learning ability of the proposed method. Simulation results show that the proposed method has an improved performance over the MP detector and achieves nearly the same performance as an optimal maximum a posteriori (MAP) detector with a very low time complexity.

Despite the enormous impact on human health, acute respiratory distress syndrome (ARDS) is ill-defined, and its timely diagnosis is difficult, as is tracking the course of the syndrome. The objective of this pilot study was to explore the utility of breath collection and analysis methodologies to detect ARDS through changes in the volatile organic compound (VOC) profiles present in breath. Five male Yorkshire mix swine were studied and ARDS was induced utilising both direct and indirect lung injury. An automated portable gas chromatography device developed in-house was used for point of care breath analysis and to monitor swine breath hourly, starting from the initiation of the experiment until the development of ARDS, which was adjudicated based on the Berlin criteria at the breath sampling points and confirmed by lung biopsy at the end of the experiment. A total of 67 breath samples (chromatograms) were collected and analyzed. Through machine learning, principal component analysis, and linear discrimination analysis, seven VOCs biomarkers were identified that distinguished ARDS. These represent seven of the nine biomarkers found in our breath analysis study of human ARDS corroborating our findings. We also demonstrated that breath analysis detects changes 1–6 h earlier than the clinical adjudication based on the Berlin criteria. The findings provide proof of concept that breath analysis can be used for the identification of early changes associated with ARDS pathogenesis in swine. Its clinical application could provide intensive care clinicians with a non-invasive diagnostic tool for early detection and continuous monitoring of ARDS.

The use of thermal insulators to prevent undesirable heat exchange and save energy is a growing trend in practically all human activities. But despite an intense period of interest in such materials over the past three decades, the literature lacks systemic and structured information that combines technological and scientific aspects of their development. The first part of this study provides a compilation of updated information on the thermal energy consumption per area of human activity and examples of industrial processes that occur in each temperature range. The second part is devoted to a classification of insulators and the most common causes of premature failure, providing a roadmap for problem-solving towards future developments.

Cerebrovascular autoregulation (CA) is a protective mechanism that enables the cerebral vasculature to automodulate tone in response to changes in cerebral perfusion pressure to ensure constant levels of cerebral blood flow (CBF) and oxygen delivery. CA can be impaired after neurological injury and contributes to secondary brain injury. In this study, we report novel impedance indices using trans-ocular brain impedance (TOBI) during controlled systemic hemorrhage and hypotension to assess CA in comparison with pressure reactivity index (PRx). Yorkshire swine were instrumented to record intracranial pressure (ICP), mean arterial pressure (MAP), and CBF. TOBI was recorded using electrocardiographic electrodes placed on the closed eyelids. Impedance changes (dz) were recorded in response to introducing an alternating current (0.4 mA) through the electrodes. MAP, ICP, and CBF were also measured. Animals were subjected to a controlled hemorrhage to remove 30–40% of each animal’s total blood volume over 25–35 min. Hemorrhage was titrated to reach an MAP of approximately 35 mm Hg and end-tidal carbon dioxide above 28 mm Hg. PRx was calculated as a moving Pearson correlation between MAP and ICP. TOBI indices were calculated as the amplitude of the respiratory-induced changes in dz. DZx was calculated as a moving Pearson correlation between dz and MAP. TOBI indices (dz and DZx) were compared with hemodynamic indicators and PRx. dz was shown to be highly correlated with MAP, ICP, cerebral perfusion pressure, and CBF (r = − 0.823, − 0.723, − 0.813, and − 0.726), respectively (p

Researchers often use theoretical models which provide a relatively sim- ple, yet concise and effective way of modelling various phenomena. However, it is a well-known fact that the more complex the model, the more complex the mathematical description is. For this reason, theoretical models generally avoid large complexity and aim for the simplest possible definition, which although makes models mathematically more manageable, in practice it also often leads to sub-optimal performance. Furthermore, the data collected during the observations usually contain confounding factors, for which a simple theoretical model can not be prepared. Overall, mathematical models are usually too rigid and sophisticated, and therefore cannot really deal with sudden changes in the environment. The application of artificial intelligence, however, provides a good opportunity to develop complex models that can combine the basic capabilities of the theoretical models with the ability to learn more complex relationships. It has been shown [16] that with neural networks, we can build such models that can approximate mathematical functions. Trained artificial neural networks are thus able to behave like theoretical models developed for different fields, while still retaining their overall flexibility, which guarantees an overall better performance in a complex realworld environment. The aim of our study is to show our notion that we can create an architecture using neural networks, which is able to approximate a given theoretical model, and then further improve it with the help of real data to suit the real world and its various aspects better. In order to validate the functionality of the architecture developed by us, we have selected a simple theoretical model, namely the Kermack-McKendrick one [4] as the base of our research. This is an SIR [2] model, which is a relatively simple compartmental epidemic model, based on differential equations that can be used well for infections that spread very similarly to influenza or COVID. However, on one hand, the SIR model relies too heavily on its parameters, with slight changes in them leading to drastic overall changes of the S, I and R curves, and on the other hand, the simplicity of the SIR model distorts its accuracy in many cases. In our paper, by using the SIR model, we will show that the architecture described above can be a valid approach to modeling the spread of a given disease (such as influenza or COVID-19). To this end, we detail the accuracy of our models with different settings and configurations and show that it performs better than both a simple mathematical model and a plain neural network with randomly initialized weights.

The last half-century has witnessed the birth and development of a new multidisciplinary field at the edge between materials science, nanoscience, engineering, and chemistry known as Molecular Electronics. This field deals with the electronic properties of individual molecules and their integration as active components in electronic circuits and has also been applied to biomolecules, leading to BioMolecular Electronics and opening new perspectives for single-molecule biophysics and biomedicine. Herein, we provide a brief introduction and overview of the BioMolecular electronics field, focusing on nucleic acids and potential applications for these measurements. In particular, we review the recent demonstration of the first single-molecule electrical detection of a biologically-relevant nucleic acid. We also show how this could be used to study biomolecular interactions and applications in liquid biopsy for early cancer detection, among others. Finally, we discuss future perspectives and challenges in the applications of this fascinating research field.

Convolutional Neural Network (CNN) for medical image classification has produced satisfying work [11, 12, 15]. Several pretrained models such as VGG19 [17], InceptionV3 [18], and MobileNet [8] are architectures that can be relied on to design high accuracy classification models. This work investigates the performance of three pretrained models with two methods of training. The first method trains the model independently, meaning that each model is given an input and trained separately, then the best results are determined by majority voting. In the second method the three pretrained models are trained simultaneously as interconnected models. The interconnected model adopts an ensemble architecture as is shown in [7]. By training multiple CNNs, this work gives optimum results compared to a single CNN. The difference is that the three subnetworks are trained simultaneously in an interconnected network and showing one expected result. In the training process the interconnected model determines each subnetwork’s weight by itself. Furthermore, this model will apply the most suitable weight to the final decision. The interconnected model showed comparable performance after training on several datasets. The measurement includes comparing the Accuracy, Precision and Recall scores as is shown in confusion matrix [3, 14].

After the sudden death of captive marmosets in São Paulo, Brazil, we conducted a histologic and microbiologic study. We found hyperacute septicemia caused by hypermucoviscous sequence type 86 K2 Klebsiella pneumoniae. We implemented prophylactic antimicrobial therapy, selected dedicated staff for marmoset interactions, and sanitized the animals’ fruit to successfully control this outbreak.

Erythroferrone (ERFE) is a hormone produced by erythroblasts in the bone marrow in response to erythropoietin controlling iron storage release through its actions on hepcidin, which acts on hepatocytes to suppress expression of the hormone hepcidin. Erythroferrone now considered is one of potential clinical biomarkers for assessing erythropoiesis activity in patients with blood disorders regarding to iron imbalance. Since discovery of in 2014 by Dr. Leon Kautz and colleagues and till now no more enough studies in Erythroferrone among human, most studies are conducted in animals. In this review we briefly address the Role of Erythroferrone hormone as erythroid regulator of hepcidin and iron metabolism during thalassemia and in iron deficiency anemia. Studies in this review were identified through a search using the following electronic databases: PubMed, Academia, Scopus, Google Scholar, and another open database source. Conclusion: Most of studies concluded that, in people with thalassemia and iron deficiency anemia, erythroferrone levels in the blood are higher than in people without thalassemia and iron deficiency anemia. Knowing the mechanisms of erythroferrone as erythroid regulator of hepcidin and iron metabolism during thalassemia and in iron deficiency anemia important in the diagnosis and treatment for both conditions. The erythroferrone hormone may act as potential factor in physiological hepcidin suppressor in cases with iron deficiency anemia and thalassemia disease and play a key role in treatment process among those patients in status of iron deficiency or iron overload. However, till now few studies of the function of ERFE in humans because is recently discovered and remains to be investigated and most studies are conducted among animals.

INTRODUCTION: In March 2020, the world health organization declared Coronavirus Disease (COVID)-19 a pandemic global communicable disease, there is neither a vaccine nor a treatment for this virus. The aim of the current study was to assess knowledge, attitude, and practice (KAP) of Iraqi population toward COVID-19. METHODS: A cross-sectional study was conducted among 272 respondents from different states of Iraq using online administered questionnaires. The questions were adopted from the previous study and consist of three parts mainly socio-demographic, KAP. RESULTS: The majority of the respondents had good knowledge regarding COVID-19 (95.2%). A total of 97.8% of the respondents knew that COVID-19 caused by a virus. Regarding incubation period, 75% of them answer correctly that it is 2–15 days. Higher percept (39%) and 37.9% of participant people considered the disease as a very dangerous and seriously dangerous disease, respectively. The majority (85.3%) of the sample thought that no vaccination available for the disease. More than two-thirds of the sample (76.5%) were wearing face mask sometimes while only 19.5% wearing it all the time. The majority (265) out of 272 was ready to stay home if it required to prevent the spread of the disease. There was a significant association between gender, living state, and COVID-19 knowledge (p = 0.009

Cerebrovascular autoregulation (CA) is often impaired following traumatic brain injury. Established technologies and metrics used to assess CA are invasive and conducive for measurement, but not for continuous monitoring. We developed a trans-ocular brain impedance (TOBI) method that may provide non-invasive and continuous indices to assess CA. In this study, we monitored impedance metrics such as respiratory-induced impedance amplitude changes (dz) as well as a novel impedance index (DZx), which is a moving Pearson correlation between mean arterial pressure (MAP) and dz. Yorkshire swine were instrumented to continuously record ICP, MAP, and cerebral blood flow (CBF). TOBI was recorded by placement of standard ECG electrodes on closed eyelids and connected to a data acquisition system. MAP, ICP and CBF were manipulated utilizing an intravenous vasopressor challenge. TOBI indices (dz and DZx) were compared to the hemodynamic indicators as well as pressure reactivity index (PRx). During the vasopressor challenge, dz was highly correlated with ICP, CPP, and CBF (r =

Introduction : Sustainable development goals are big challenge to the nation and they represent a critical encounter to be achieved with the current unsustainable condition worldwide The aim of the current study was to find out the level of knowledge regarding sustainable development goals (SDG) among medical students at a private university in Shah Alam, Malaysia. Methods: A cross-sectional study was conducted among 203 medical students at a Private University in Selangor Malaysia using convince sampling method. Results: Only (77.8%) of the respondents heard of Sustainable development goals (SDG) and the main source of information was internet (35.5%). Two-thirds of them knew that SDG should be achieved by year 2030. Only half of the respondents knew the correct numbers of SDG which is 17 goals and (45.3%) knew that SDG consist of 169 targets. Conclusion: as a conclusion, medical students have adequate knowledge regarding sustainable development goals, but more education and promotion are needed, especially for future medical doctors as they will be the frontlines in achieving those goals.

Breast cancer is one of the leading causes of mortality among women. The aim of the current study was to assess the factors affecting awareness and beliefs regarding breast cancer, among women living in the state of Selangor, Malaysia. A cross-sectional study was conducted among women living in Selangor aged 18 to 65 years old, with a total of 483 participants. A validated questionnaire regarding awareness and belief was used. The questionnaires consist of 25 questions comprising of socio-demographic, history of other diseases, awareness and belief. The prevalence of poor awareness among women was 63.4%. while the poor level of belief was 84.7%. There was a significant association between awareness and educational background, family history of breast cancer (P=0.001, P=0.032) respectively. The association between awareness and belief was significant (P=0.02). As a conclusion: There is a high level of poor belief and poor awareness among women in Selangor, Malaysia regarding breast cancer. Source of information such as the internet plays a major role in breast cancer prevention, and the majority of them do not know the technique of breast self-examination. More health promotion is needed to target general population through big campaign of awareness.

Background: Once patients have a diagnosis of localization related epilepsy (LRE), it is critical to further classify those patients into lesional or non-lesional for treatment and prognostic reasons. The cornerstone of lesion detection in chronic epilepsy is structural imaging, mainly magnetic resonance imaging. Molecular & Metabolic information from Diffusion weighted MRI & magnetic resonance spectroscopy (MRS) might serve as an additional or as a surrogate marker for the epileptogenic lesion. Methods: 70 patients presented to Aljahra general hospital, Jahra city, Kuwait and were referred to radiology department for MRI study patients represented spectrum for different causes of epilepsy & different age groups All patients was scanned using Semens Aera 1.5 tesla & skyra 3 tesla magnets by conventional MRI ((T1WI, T2WI, FALIR), Diffusion weight images ( DWI/ADC) sequences & MRS were done for patients with SOL, suspected vascular or inflammatory causes. The patients with fits with no MRI signal or structural abnormality are excluded from this study. Results: DWI & MRS can provide additional diagnostic information that may facilitate and support the final diagnosis, especially if clinical symptoms are inconclusive. While conventional imaging provided only anatomical information, Conclusion: Diffusion weighted images (DWI) & SWI with conventional MRI increased accuracy of diagnosis of lesional epilepsy.

Brazil has a predominantly renewable energy matrix, with large participation of water resource in domestic supply of energy. Data from 2019 National Energy Balance show that renewable sources (water, biomass, wind and solar photovoltaic) together represented 83% of domestic electric supply in 2018, where the remaining percentage (16.7%) represented non-renewable sources. The generation of electricity through thermal solar technology was not representative. However, it is known that Brazil presents high potential for the installation of solar thermal plants, especially in the Northeastern Region, where direct normal solar irradiation values are high. It is observed that the (high) costs of the projects associated to the absence of a specific incentive program make Concentrating Solar Power (CSP) plants installation more and more time consuming. As a way to contribute to the insertion of solar thermal energy in Brazil, this article presents a methodology for the location of parabolic trough solar thermal plants of 80 MW for the State of Bahia, located in the Northeastern Region of Brazil. Such methodology was based on the application the Analytic Hierarchy Process (AHP) method and the Geoprocessing Technologies to define potentially available sites for the implementation of the projects. For the analysis, parameters related to energy production in the solar power plant, electric, roadways and water infrastructure of the plant were taken into account, as well as the occupation, slopes and land use. Considering the analyses performed, it was confirmed that Bahia disposes of many sites with great generation potential, especially in the western region of the State (at Barreiras), where favorable conditions were found for the development of the technology. Localities situated in other region of the State were also confirmed as suitable, however with less site availability for the insertion of plants. Methodology validations were also carried out and indicated that the model reached the proposed objective, faithfully representing the real-world simplifications that were made.

BACKGROUND Non-compressible torso hemorrhage management remains a challenge especially in the prehospital setting. We evaluated a device designed to occlude the aorta from the stomach (Gastroesophageal Resuscitative Occlusion of the Aorta (GROA)) for its ability to stop hemorrhage and improve survival in a swine model of lethal liver laceration and compared its performance to Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) and controls. METHODS Swine (n = 24) were surgically instrumented and a 30% controlled arterial hemorrhage over 20-minutes was followed by liver laceration. Animals received either GROA, REBOA, or control (no treatment) for 60-minutes. Following intervention, devices were deactivated, and animals received whole blood and crystalloid resuscitation. Animals were monitored for an additional four hours. RESULTS The liver laceration resulted in the onset of class IV shock. Mean arterial blood pressure ((MAP) (standard deviation)) decreased from 84.5 mmHg (11.69 mmHg) to 27.1 mmHg (5.65 mmHg) at the start of the intervention. Seven of eight control animals died from injury prior to the end of the intervention period with a median survival (interquartile) time of 10.5 minutes (12 minutes). All GROA and REBOA animals survived the duration of the intervention period (60-minutes) with median survival times of 86 minutes (232 minutes) and 79 minutes (199 minutes) after resuscitation, respectively. GROA and REBOA animals experienced a significant improvement in survival compared to controls (p = 0.01). REBOA resulted in higher MAP at the end of intervention 114.6 mmHg (22.9 mmHg) compared to GROA 88.2 mmHg (18.72 mmHg), (p = 0.024) as well as increased lactate compared to GROA 13.2 meq/L (1.56 meq/L) vs. 10.5 meq/L (1.89 meq/L), (p = 0.028). Histological examination of the gastric mucosa in surviving animals revealed mild ischemic injury from both GROA and REBOA. CONCLUSIONS GROA and REBOA devices were both effective at temporarily stanching lethal NCTH of the abdomen and prolonging survival. LEVEL OF EVIDENCE Basic Science; Does not require a level of evidence.

This systematic review focuses on different genetic mutations identified in studies on natural short sleepers, who would not be ill-defined as one type of sleep-related disorder. The reviewed literature is from databases such as PubMed, PMC, Scopus, and ResearchGate. Due to the rare prevalence, the number of studies conducted on natural short sleepers is limited. Hence, searching the search of databases was done without any date restriction and included animal studies, since mouse and fly models share similarities with human sleep behaviors. Of the 12 articles analyzed, four conducted two types of studies, animal and human (cross-sectional or randomized-controlled studies), to testify the effects of human mutant genes in familial natural short sleepers via transgenic mouse or fly models. The remaining eight articles mainly focused on one type of study each: animal study (four articles), cross-sectional study (two articles), review (one article), and case report (one article). Hence, those articles brought different perspectives on the natural short sleep phenomenon by identifying intrinsic factors like DEC2, NPSR1, mGluR1, and β1-AR mutant genes. Natural short sleep traits in either point-mutations or single null mutations in those genes have been examined and confirmed its intrinsic nature in affected individuals without any related health concerns. Finally, this review added a potential limitation in these studies, mainly highlighting intrinsic causes since one case study reported an extrinsically triggered short sleep behavior in an older man without any family history. The overall result of the review study suggests that the molecular mechanisms tuned by identified sleep genes can give some potential points of therapeutic intervention in future studies.

An alternate direction method of multipliers (ADMM)-based detectors can achieve good performance in both small and large-scale multiple-input multiple-output (MIMO) systems. However, due to the difficulty of choosing the optimal penalty parameters, their performance is limited. This paper presents a deep neural network (DNN)-based massive MIMO detection method which can overcome the above limitation. It exploits the unfolding technique and learns to estimate the penalty parameters. Additionally, a computationally cheaper detector is also proposed. The proposed methods can handle the higher-order modulation signals. Numerical results are presented to demonstrate the performances of the proposed methods compared with the existing works.

Among the electrical problems observed from the solar irradiation variability, the electrical energy quality and the energetic dispatch guarantee stand out. The great revolution in batteries technologies has fostered its usage with the installation of photovoltaic system (PVS). This work presents a proposition for voltage regulation for residential prosumers using a set of scalable power batteries in passive mode, operating as a consumer device. The mitigation strategy makes decisions acting directly on the demand, for a storage bank, and the power of the storage element is selected in consequence of the results obtained from the power flow calculation step combined with the prediction of the solar radiation calculated by a recurrent neural network Long Short-Term Memory (LSTM) type. The results from the solar radiation predictions are used as subsidies to estimate, the state of the power grid, solving the power flow and evidencing the values of the electrical voltages 1-min enabling the entry of the storage device. In this stage, the OpenDSS (Open distribution system simulator) software is used, to perform the complete modeling of the power grid where the study will be developed, as well as simulating the effect of the overvoltages mitigation system. The clear sky day stored 9111 Wh/day of electricity to mitigate overvoltages at the supply point, when compared to other days, the clear sky day needed to store less electricity. On days of high variability, the energy stored to regulate overvoltages was 84% more compared to a clear day. In order to maintain a constant state of charge (SoC), it is necessary that the capacity of the battery bank be increased to meet the condition of maximum accumulated energy. Regarding the total loading of the storage system, the days of low variability consumed approximately 12% of the available capacity of the battery, considering the SoC of 70% of the capacity of each power level.

Chronic renal disease (CRD) is a pathophysiologic process with multiple etiologies, resulting in the inexorable attrition of Nephron number and function and frequently leading to end-stage renal disease (ESRD). In turn, ESRD represents a clinical state or condition in which there has been an irreversible loss of endogenous renal function, of a degree sufficient to render the patient permanently dependent upon renal replacement therapy (dialysis of transplantation) in order to avoid life threatening uremia, reflecting a dysfunction of all organ systems as a result of untreated or under treated acute or chronic renal failure. The current study was involved 80 patients, the age range within 25-70 years, selected sample of patients who attend Iraqi center of kidney dialysis, Baghdad Teaching Hospital and Al-Yarmok Teaching Hospital. All the patient's body mass index [BMI] were measured serum anti diuretic hormone ADH was assessed using enzyme linked immunosorbent kit [Elisa] while urea, creatinine, albumin, hemoglobin were determined by spectrophotometer (PD-303), and sodium, potassium, chloride were determined by Electrolyte analyzer 9180.These patients were divided in two group B (40 patients) as pre dialysis with ESRD, and group A (40) healthy control. Results showed non-significant elevations in ADH levels in the patient group compared with the control group, while highly significant increase in urea, creatinine and albumin levels in in the patient group compared with the control group, also highly significant decrease in hemoglobin comparing with the control group. Also, it was observed non-significant decrease in sodium, non-significant increase in potassium and highly significant increase in chloride when comparing with the control group.

Vitamin D is a fat-soluble vitamin that has many important functions. The main objective of the study is to find out mothers’ attitude towards vitamin D among infants in Baghdad city, Iraq. A cross-sectional study was carried out among 771 mothers in Baghdad city, Iraq. Questionnaire related to vitamin were distributed. The mean age of mothers was 28.30± 3.9 years. Majority of the mothers (80.9%) gave vitamin D supplements to their infants and around 76.4% their pediatrician advised them to give vitamin D supplement. There was a significant association between giving vitamin D supplement with mother’s educational level, pediatrician advice and start walking (p = 0.03

The purposes of this study are to determine the effect of Zantac (Ranitidine) drug on sexual hormones (Testosterone and Prolactin), sperm head abnormality, and histopathological activity on albino male mice testes. Two doses of the drug were used: 1000 and 2000 mg/kg, in addition to a negative control group. Each group included four mice and the drug administrated orally as (0.1 ml) per day for 14 days, and then the mice were sacrificed on the day 15 for laboratory assessment. The result showed that the drug cause increase the percentage of sperm head abnormality which reaches to (48.3%) and (22.8%) for 2000 mg/kg and 1000 mg/kg respectively in comparison to control group (11.1%). The male sex hormones also affected by the drug and the level of testosterone hormone decrease to (1.07 ng/ml) in 2000 mg/kg and (3.42 ng/ml) in 1000 mg/kg, while the level of hormone in control group is (14.07 ng/ml). The prolactin hormone show increase in the level at dose 2000mg/kg it's (115 ng/ml) and in dose 1000 mg/kg the level its (43 ng/ml) these value very high compared with control group (36 ng/ml) due to the effect of the drug on the hormone. The histopathological examination shows damage in the seminal duct and changes in the wall of the seminiferous tube also the spermatogenesis well be affected by the drug in which is stop and some of seminal duct show no appearance of spermatogenesis and also cause depletion to the wall of the seminal duct.

To date, existing animal models of the acute respiratory distress syndrome (ARDS) have failed to translate preclinical discoveries into effective pharmacotherapy or diagnostic biomarkers. To address this translational gap, we developed a high‐fidelity swine model of ARDS utilizing clinically relevant lung injury exposures. Fourteen male swine were anesthetized, mechanically ventilated, and surgically instrumented for hemodynamic monitoring, blood, and tissue sampling. Animals were allocated to one of three groups: (1) Indirect lung injury only: animals were inoculated by direct injection of Escherichia coli into the kidney parenchyma, provoking systemic inflammation and distributive shock physiology; (2) Direct lung injury only: animals received volutrauma, hyperoxia, and bronchoscope‐delivered gastric particles; (3) Combined indirect and direct lung injury: animals were administered both above‐described indirect and direct lung injury exposures. Animals were monitored for up to 12 h, with serial collection of physiologic data, blood samples, and radiographic imaging. Lung tissue was acquired postmortem for pathological examination. In contrast to indirect lung injury only and direct lung injury only groups, animals in the combined indirect and direct lung injury group exhibited all of the physiological, radiographic, and histopathologic hallmarks of human ARDS: impaired gas exchange (mean PaO2/FiO2 ratio 124.8 ± 63.8), diffuse bilateral opacities on chest radiographs, and extensive pathologic evidence of diffuse alveolar damage. Our novel porcine model of ARDS, built on clinically relevant lung injury exposures, faithfully recapitulates the physiologic, radiographic, and histopathologic features of human ARDS and fills a crucial gap in the translational study of human lung injury., Our novel porcine model of the acute respiratory distress syndrome, built on clinically‐relevant lung injury exposures, faithfully recapitulates the physiologic, radiographic, and histopathologic features of human ARDS, and fills a crucial gap in the translational study of human lung injury.

The single axis solar tracker based on flat panels is used in large solar plants and in distribution-level photovoltaic systems. In order to achieve this, the solar tracking systems generally need to work by tracking the sun’s position with dozens, maybe hundreds of movements along the day with a maximal known tracking error within the specifications. A novel model is proposed along this work based on the control of the angle deviation within a (polar) single axis configuration. This way an optimization of the harnessing of solar energy can be achieved with as few panel displacements as possible in order to decrease the wear in the mechanical parts of the equipment and the energy consumed by it. This tracking approach was implemented with as few as seven positions along the day and got an estimated theoretical value of 99.27% of the total collected energy in a continuous tracking system. Regarding an annual average basis, it would be about 96.5% of a dual axis system according to the proposed model. The novelty of the model is related to a tradeoff between the gain with the simplicity of a single axis n-position tracking and the solar energy loss associated.

International audience; Motivated by the gained momentum of the sustainable development goals (SDGs) under the umbrella of the United Nations organization, in the light of the SDG‐7, which stipulates the access to affordable, sustainable, and modern energy, this paper explores the dynamic relationship between renewable energy and the pillars of sustainable development. Its insights are driven using a simultaneous equation model based on a panel of 25 African economies covering the period 1990–2014. The results show that renewable energy is important for sustainable development and that higher levels of renewable energy can increase sustainability. The findings also confirm the positive influence of renewable energy on the economic, environmental, social, and institutional dimensions. These positive effects stem from investment in clean energy in the whole of Africa, combined with structural changes promoting the use of clean energy and the achievement of the millennium development goals. The findings should be useful for policymakers in Africa. Aggressive renewable energy policies will be crucial for achieving energy‐policy goals and the “multiple benefits” of renewable energy, such as reducing climate change and air pollution, improving energy security, and increasing access to energy.

Background Seasonal influenza vaccination (SIV) rates remain suboptimal in many populations, even in those with universal SIV. Objective To summarize the evidence on interventions on health care providers (physicians/nurses/pharmacists) to increase SIV rates. Methods We systematically searched/selected full-text English publications from January 2000 to July 2019 (PROSPERO-CRD42019147199). Our outcome was the difference in SIV rates between patients in intervention and non-intervention groups. We calculated pooled difference using an inverse variance, random-effects model. Results We included 39 studies from 8370 retrieved citations. Compared with no intervention, team-based training/education of physicians significantly increased SIV rates in adult patients: 20.1% [7.5–32.7%; I2 = 0%; two randomized controlled trials (RCTs)] and 13.4% [8.6–18.1%; I2 = 0%; two non-randomized intervention studies (NRS)]. A smaller increase was observed in paediatric patients: 7% (0.1–14%; I2 = 0%; two NRS), and in adult patients with team-based training/education of physicians and nurses together: 0.9% (0.2–1.5%; I2 = 30.6%; four NRS). One-off provision of guidelines/information to physicians, and to both physicians and nurses, increased SIV rates in adult patients: 23.8% (15.7–31.8%; I2 = 45.8%; three NRS) and paediatric patients: 24% (8.1–39.9%; I2 = 0%; two NRS), respectively. Use of reminders (prompts) by physicians and nurses slightly increased SIV rates in paediatric patients: 2.3% (0.5–4.2%; I2 = 0%; two RCTs). A larger increase was observed in adult patients: 18.5% (14.8–22.1%; I2 = 0%; two NRS). Evidence from both RCTs and NRS showed significant increases in SIV rates with varied combinations of interventions. Conclusions Limited evidence suggests various forms of physicians’ and nurses’ education and use of reminders may be effective for increasing SIV rates among patients.

BackgroundEvidence suggests that repeated influenza vaccination may reduce vaccine effectiveness (VE). Using influenza vaccination program maturation (PM; number of years since program inception) as a proxy for population-level repeated vaccination, we assessed the impact on pooled adjusted end-season VE estimates from outpatient test-negative design studies.MethodsWe systematically searched and selected full-text publications from January 2011 to February 2020 (PROSPERO: CRD42017064595). We obtained influenza vaccination program inception year for each country and calculated PM as the difference between the year of deployment and year of program inception. We categorized PM into halves (cut at the median), tertiles, and quartiles and calculated pooled VE using an inverse-variance random-effects model. The primary outcome was pooled VE against all influenza.ResultsWe included 72 articles from 11 931 citations. Across the 3 categorizations of PM, a lower pooled VE against all influenza for all patients was observed with PM. Substantially higher reductions were observed in older adults (≥65 years). We observed similar results for A(H1N1)pdm09, A(H3N2), and influenza B.ConclusionsThe evidence suggests that influenza VE declines with vaccination PM. This study forms the basis for further discussions and examinations of the potential impact of vaccination PM on seasonal VE.

Background Non-compressible torso hemorrhage (NCTH) of the abdomen is a challenge to rapidly control and treat in the prehospital and emergency department settings. In this pilot study, we developed a novel intra-peritoneal hemostasis device (IPHD) prototype and evaluated its ability for slowing NCTH and prolonging survival in a porcine model of lethal abdominal multi-organ hemorrhage. Methods Yorkshire male swine (n=8) were instrumented under general anesthesia for monitoring of hemodynamics and blood sampling. Animals were subjected to a 30% controlled arterial hemorrhage followed by lacerating combinations of the liver, spleen, and kidney. The abdomen was closed and after 2 minutes of NCTH, the IPHD was inserted into the peritoneal cavity via an introducer (n=5). The balloon was inflated and maintained for 60 minutes. At 60-minutes post deployment the balloon was deflated and removed, and blood resuscitation was initiated followed by gauze packing for hemostasis. The remaining animals (n=3) were used as controls and subjected to the same injury without intervention. Results All animals managed with IPHD intervention (5/5) survived the duration of the intervention period (60 minutes) while all control animals (3/3) died at a time range of 15-43 minutes following organ injury (p=0.0042). Animals receiving IPHD remained hemodynamically stable with a mean arterial pressure range of 44.86 to 55.10mmHg, experienced increased cardiac output and decreased shock index after treatment. Controls experienced hemodynamic decline in all parameters until endpoints were met. Upon IPHD deflation and removal, all treated animals began to hemorrhage again and expired within 2-132 minutes despite packing. Conclusions Our data shows that the IPHD concept is capable of prolonging survival by temporarily stanching lethal NCTH of the abdomen. This device may be an effective temporary countermeasure to NCTH of the abdomen that could be deployed in the pre-hospital environment or as a bridge to more advanced therapy. Level of evidence Basic Science; Does not require a level of evidence. Study type Therapeutic device.

To date, existing animal models of the acute respiratory distress syndrome (ARDS) have failed to translate preclinical discoveries into effective pharmacotherapy or diagnostic biomarkers. To address this translational gap, we developed a high-fidelity swine model of ARDS utilizing clinically-relevant lung injury exposures. Fourteen male swine were anesthetized, mechanically ventilated, and surgically instrumented for hemodynamic monitoring, blood, and tissue sampling. Animals were allocated to one of three groups: 1) Indirect lung injury only: animals were inoculated by direct injection of E. coli into the kidney parenchyma, provoking systemic inflammation and distributive shock physiology; 2) Direct lung injury only: animals received volutrauma, hyperoxia, and bronchoscope-delivered gastric particles; 3) Combined indirect and direct lung injury: animals were administered both above-described indirect and direct lung injury exposures. Animals were monitored for up to 12 hours, with serial collection of physiologic data, blood samples, and radiographic imaging. Lung tissue was acquired post-mortem for pathological examination. In contrast to indirect lung injury only and direct lung injury only groups, animals in the combined indirect and direct lung injury group exhibited all of the physiological, radiographic, and histopathologic hallmarks of human ARDS: impaired gas exchange (mean PaO2/FiO2 ratio 124.8 ± 63.8), diffuse bilateral opacities on chest radiographs, and extensive pathologic evidence of diffuse alveolar damage. Our novel porcine model of ARDS, built on clinically-relevant lung injury exposures, faithfully recapitulates the physiologic, radiographic, and histopathologic features of human ARDS, and fills a crucial gap in the translational study of human lung injury.

In this study, the behavior of a water supply system with PV technology was analyzed and modeled on different meteorological conditions. The photovoltaic water supply system, PVWSS, consists of a PV generator set, a frequency inverter, a motor pump and a storage system. This water supply system was thoroughly experimentally tested, on a test bench in real operating conditions (outdoor) in Recife, Brazil. The meteorological and geometric parameters of the system and its flow rates results were used to perform a modeling through ANN to estimate the pumped volume in function to meteorological conditions and the discharge heads. The ANN modeling predicted the flow rates with percentage deviation and normalized mean square error of − 0.6% and 6.85%, respectively. Using a residential water demand curve, the system was simulated for different meteorological conditions, resulting in a deficit of 0.2%, a unit cost of $0.29 per m3 pumped to a discharge head of 24 m with an optimum reservoir of 2 days of autonomy. This model can predict the flow rates for any location and weather conditions.

Pensar em currículo integrado é pensar em integração de experiências e conhecimentos enriquecedores, através da prática pedagógica assistida, planejamento coletivo, criação de projetos integradores, programas de iniciação científica e de extensão, projetos de pesquisa, práticas profissionais integradas, entre outras práticas inovadoras utilizadas para o alcance dos objetivos do curso. Este projeto integrador é sobre o desenvolvimento de um software para gerenciar as feiras de alimentos do município de Iporá-GO. O objetivo é desenvolver um aplicativo que facilite a vida das pessoas que tem o costume de frequentar as feiras de alimentos, mostrando as bancas com seus devidos produtos, a sua origem e o valor, bem como melhorar a relação entre consumidores e a agricultura familiar. No primeiro momento, levantou-se as feiras realizadas na cidade de Iporá e o perfil dos comerciantes. No segundo momento, fez a análise do sistema com os dados levantados. No terceiro e último momento, desenvolveu o aplicativo na linguagem Delphi com banco de dados SQlite. O aplicativo mostrou-se que atende ao problema proposto e está de acordo com os conceitos do UX.

In this work, we investigated the ability of several Convolutional Neural Network (CNN) models for predicting the spread of cancer using medical images. We used a dataset released by the Kaggle, namely PatchCamelyon. The dataset consists of 220,025 pathology images digitized by a tissue scanner. A clinical expert labeled each image as cancerous or non-cancerous. We used 70% of the images as a training set and 30% of them as a validation set. We design three models based on three commonly used modules: VGG, Inception, and Residual Network (ResNet), to develop an ensemble model and implement a voting system to determine the final decision. Then, we compared the performance of this ensemble model to the performance of each single model. Additionally, we used a weighted majority voting system, where the final prediction is equal to the weighted average of the prediction produced by each network. Our results show that the classification of the two ensemble models reaches 96%. Thus these results prove that the ensemble model outperforms single network architectures.

Objectives The COVID-19 pandemic is considered a major threat to global public health. The aim of our study was to use the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data. Methods We used publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we employed RNNs with gated recurring units (Long Short-Term Memory - LSTM units) to create two prediction models. Our proposed approach considers an ensemble-based system, which is realized by interconnecting several neural networks. To achieve the appropriate diversity, we froze some network layers that control the way how the model parameters are updated. In addition, we could provide country-specific predictions by transfer learning, and with extra feature injections from governmental constraints, better predictions in the longer term are achieved. We have calculated the Root Mean Squared Logarithmic Error (RMSLE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to thoroughly compare our model predictions with the observed data. Results We reported the predicted curves for France, Germany, Hungary, Italy, Spain, the United Kingdom, and the United States of America. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seem to be low. Conclusion Our proposed model has shown satisfactory accuracy in predicting the new cases of COVID-19 in certain contexts. The influence of this pandemic is significant worldwide and has already impacted most life domains. Decision-makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based models are useful tools for forecasting epidemics as these models can be recalculated according to the newly observed data to get a more precise forecasting.

Ground images with a sky camera have become common to evaluate cloud coverage, aerosols, and energy collection. In parallel, the growth of solar energy has led to an impulse to evaluate and forecast the solar potential in a site before investments, which has increased the importance of solar power measurements. Facing that scenario, this work presents a novel sky camera model that allows to measure the global horizontal irradiance (GHI). Initially, images from a fisheye camera were stored and a pixel-based approach model was created for cloud segmentation. A total of 813 k vectors of features were used as input to the support vector machine for classification (SVC), which yielded a success rate of about 98.6% in accuracy. The Sun’s position was also segmented and an artificial neural network (ANN) regression model for GHI with 17 input features was created based on segmentation of the Sun, clouds, and sky. The training/validation stage of the ANN used 89,964 samples and the test stage reached about 97.4% in Pearson’s correlation. The RMSE was 72.3 W/m2 for GHI and the normalized RMSE, nRMSE, revealed 12.9% for GHI. That nRMSE value was comparable to or lower than other studies, despite the high fluctuations in the observed GHI.

BACKGROUND In this study, we examined the ability of resonance Raman spectroscopy to measure tissue hemoglobin oxygenation (R-StO2) noninvasively in critically ill patients and compared its performance with conventional central venous hemoglobin oxygen saturation (ScvO2). METHODS Critically ill patients (n = 138) with an indwelling central venous or pulmonary artery catheter in place were consented and recruited. R-StO2 measurements were obtained by placing a sensor inside the mouth on the buccal mucosa. R-StO2 was measured continuously for 5 min. Blood samples were drawn from the distal port of the indwelling central venous catheter or proximal port of the pulmonary artery catheter at the end of the test period to measure ScvO2 using standard co-oximetry analyzer. A regression algorithm was used to calculate the R-StO2 based on the observed spectra. RESULTS Mean (SD) of pooled R-StO2 and ScvO2 were 64(7.6) % and 65(9.2) % respectively. A paired t test showed no significant difference between R-StO2 and ScvO2 with a mean(SD) difference of -1(7.5) % (95% CI: -2.2, 0.3%) with a Clarke Error Grid demonstrating 84.8% of the data residing within the accurate and acceptable grids. Area under the receiver operator curve for R-StO2's was 0.8(0.029) (95% CI: 0.7, 0.9 P

Introduction: Respiratory infections can occur through the transmission of virus-containing aerosols from infected individuals. It remains unclear whether cardiac arrest (CA) resuscitation generates aerosols that have the potential to transmit diseases including COVID-19. Hypothesis: In a swine CA model, chest compression and defibrillation generate aerosols (≤10 μm) that could contain respiratory viruses. Methods: To simulate witnessed CA with bystander-initiated CPR, 3 female non-intubated swine underwent 4 minutes of untreated ventricular fibrillation CA followed by ten 2-minute cycles of mechanical chest compression and defibrillation without ventilation. The diameter (0.3 - 10 μm) and quantity of aerosols generated during 45-second intervals of no-flow, chest compression, and after each defibrillation were collected via a nose cone by the TSI Optical Particle Sizer. Aerosol generated from the coughs of 4 healthy human subjects were also compared to the swine data. Results: There was no significant difference between the total aerosols generated during chest compression compared to no-flow. In contrast, defibrillation followed by chest compression generated significantly more aerosols than chest compression or no-flow alone (72.4 ± 41.6 x10 4 vs 12.3 ± 8.3 x10 4 vs 10.5 ± 11.2 x10 4 ; p < 0.0001), particularly at the larger-sized aerosols. Two consecutive human coughs generated 54.7 ± 33.9 x10 4 aerosols with a size distribution much smaller than post-defibrillation CPR (Table). Conclusion: Chest compression generated comparable aerosols to no-flow in a swine CA model. In contrast, defibrillation followed by chest compression generated significantly more aerosols than chest compression or no-flow alone, with a distribution shift toward larger aerosols. Additional research is needed to elucidate the mechanisms by which aerosol generation during chest compression is modified by defibrillation and to assess rescuer risk during CA resuscitation.

The operation of charging infrastructure is often not profitable yet. However, the integration of charging infrastructure in microgrids enables the introduction of innovative business models, e. g. by using PV generation and storage units. This study presents several business models, i. a. based on self-supply of electricity and smart charging algorithms. These are evaluated by means of real-world transaction data of the EUREF campus microgrid in Berlin, Germany. The dataset comprises data of 3346 transactions of foremost commercial fleet vehicles from the year 2018. The microgrid configurations evaluated in this study involve the integration of PV generation, battery storage, individually as well as combined. A reference is provided by a comparison to the conventional operation of charging infrastructure. The evaluation shows that the integration of PV generation in combination with smart charging is profitable, due to lower expenditures by local energy supply utilization. Additional integration of a stationary battery is less profitable. However, it increases autarky and flexibility by charge during evening and night hours.

We are in this work proposing a modelling approach to conceptualize and to size a solar power farm assuming the electricity that the irrigation systems needs for their running. The model presented is formed from three principal compartments. The first permits to calculate the real water needed by the vegetable canopy formed from three stories (palm date, fruit trees, market gardening), the second permits to calculate the localized irrigation network and the energy needed to supply the water flux required, the third permits to conceptualize and to size the solar unit providing that energy. Results from a case study for Nefzawa oasis (Jemna and Rejim Maatoug) are discussed.

Background Resuscitative endovascular balloon occlusion of the aorta (REBOA) has been shown to be effective for management of noncompressible torso hemorrhage. However, this technique requires arterial cannulation, which can be time-consuming and not amendable to placement in austere environments. We present a novel, less invasive aortic occlusion device and technique designated gastroesophageal resuscitative occlusion of the aorta (GROA). In this study, we aimed to characterize the physiological tolerance and hemodynamic effects of a prototype GROA device in a model of severe hemorrhagic shock and resuscitation and compare with REBOA. Methods Swine (N = 47) were surgically instrumented for data collection. A 35% controlled arterial hemorrhage was followed by randomizing animals to 30-minute, 60-minute, or 90-minute interventions of GROA, REBOA, or control. Following intervention, devices were deactivated, and animals received whole blood and crystalloid resuscitation. Animals were monitored for an additional 4 hours. Results All animals except one GROA 90-minute application survived the duration of their intervention periods. Survival through resuscitation phase in GROA, REBOA, and control groups was similar in the 30-minute and 60-minute groups. The 90-minute occlusion groups exhibited deleterious effects upon device deactivation and reperfusion with two GROA animals surviving and no REBOA animals surviving. Mean (SD) arterial pressure in GROA and REBOA animals increased across all groups to 98 (31.50) mm Hg and 122 (24.79) mm Hg, respectively, following intervention. Lactate was elevated across all GROA and REBOA groups relative to controls during intervention but cleared by 4 hours in the 30-minute and 60-minute groups. Postmortem histological examination of the gastric mucosa revealed mild to moderate inflammation across all GROA groups. Conclusion In this study, the hemodynamic effects and physiological tolerance of GROA was similar to REBOA. The GROA device was capable of achieving high zone II full aortic occlusion and may be able to serve as an effective method of aortic impingement.

O curso de Tecnologia em Análise e Desenvolvimento de Sistemas do Instituto Federal Goiano Campus Iporá é presencial, dividido em 6 períodos, com carga horária de 2.270 horas, com 33 disciplinas obrigatórias, 200 horas de disciplinas optativas, 120 horas de atividades complementares e 150 horas de desenvolvimento do trabalho de curso. A matriz curricular do curso determina uma visão geral do curso retratando os períodos e suas respectivas disciplinas, o trabalho de curso, as atividades complementares, a carga horária e os certificados parciais com suas respetivas horas. Neste sentido, o presente trabalho tem por objetivo descrever alguns softwares desenvolvidos pelo discentes do curso assim como relacionar os conteúdos de algumas disciplinas no desenvolvimento de software. Metodologicamente, o estudo classifica-se como sendo de natureza qualitativa, por meio da realização de um relato de experiência com os discentes do curso de Tecnologia em Análise e Desenvolvimento de Sistemas do Instituto Federal Goiano, demonstrando as etapas do processo de desenvolvimento de softwares. Verificou-se, portanto, que o desenvolvimento e utilização dos conteúdos das disciplinas, contribui para a execução das atividades de desenvolvimento e para a qualidade dos trabalhos desenvolvidos.

Aim It remains unclear whether cardiac arrest (CA) resuscitation generates aerosols that can transmit respiratory pathogens. We hypothesize that chest compression and defibrillation generate aerosols that could contain the SARS-CoV-2 virus in a swine CA model. Methods To simulate witnessed CA with bystander-initiated cardiopulmonary resuscitation, 3 female non-intubated swine underwent 4 min of ventricular fibrillation without chest compression or defibrillation (no-flow) followed by ten 2-min cycles of mechanical chest compression and defibrillation without ventilation. The diameter (0.3–10 μm) and quantity of aerosols generated during 45-s intervals of no-flow and chest compression before and after defibrillation were analyzed by a particle analyzer. Aerosols generated from the coughs of 4 healthy human subjects were also compared to aerosols generated by swine. Results There was no significant difference between the total aerosols generated during chest compression before defibrillation compared to no-flow. In contrast, chest compression after defibrillation generated significantly more aerosols than chest compression before defibrillation or no-flow (72.4 ± 41.6 × 104 vs 12.3 ± 8.3 × 104 vs 10.5 ± 11.2 × 104; p

The development of micro grids is attracting increased research interest as they could assure high energy reliability, control loads in future electric power distribution systems, and foster the integration of renewable and decentralized energy resources. The impact of micro grid concepts is expanding through the increase of renewable generation systems market share and the growing limitations of the power system by higher adoption of electric vehicles. A significant challenge with regards to micro grids is the development and implementation of control algorithms to ensure efficient and reliable operation. This paper presents and investigates operational strategies, namely electric vehicle load management and self-consumption, of micro grids for real-world site applications on the European Energy Forum (EUREF) Campus in Berlin, Germany. These strategies meet requirements for economical and research operation, based on the current legal conditions for renewable energy resources in Germany. A rule-based control algorithm for self-consumption of local charging stations for electric vehicles is developed and implemented in a supervisory control and data acquisition (SCADA) environment. Measurements are performed with high resolution and analyzes carried out for the collected data sets of the years 2017-2019. A discussion shows the potential of the proposed control algorithm with regards to self-consumption. The influence of operating characteristics and restrictions in real-world applications are investigated and compared to ideal operating conditions.

Car crash can cause serious and severe injuries that impact people every day. Those injuries could be especially damaging for elderly drivers of age 60 or more. The goal of this research is to investigate the risk factors that contribute to crash injury severity among elderly drivers. This is accomplished by designing accurate machine learning based predictive models. Naive Bayesian (NB), Decision Tree (DT), Logistic Regression (LR), Light-GBM, and Random Forest (RF) model are proposed. A set of influential factors are selected to build the five predictive models to classify the severity of injuries as severe injury or non-severe injury. Michigan traffic data of the elderly population is used in this paper. Data normalization and Synthetic Minority Oversampling Technique (SMOTE) as injury classes balancing technique are used in the pre-processing phase. Results show that the Light-GBM achieved the highest accuracy among the five tested models with 87%. According to the Light-GBM model, the three most important factors that impact the severity of injuries are the driver's age, traffic volume, and car's age.

Objectives The current form of severe acute respiratory syndrome called coronavirus disease 2019 (COVID-19) caused by a coronavirus (SARS-CoV-2) is a major global health problem. The aim of our study was to use the official epidemiological data and predict the possible outcomes of the COVID-19 pandemic using artificial intelligence (AI)-based RNNs (Recurrent Neural Networks), then compare and validate the predicted and observed data. Materials and Methods We used the publicly available datasets of World Health Organization and Johns Hopkins University to create the training dataset, then have used recurrent neural networks (RNNs) with gated recurring units (Long Short-Term Memory – LSTM units) to create 2 Prediction Models. Information collected in the first t time-steps were aggregated with a fully connected (dense) neural network layer and a consequent regression output layer to determine the next predicted value. We used root mean squared logarithmic errors (RMSLE) to compare the predicted and observed data, then recalculated the predictions again. Results The result of our study underscores that the COVID-19 pandemic is probably a propagated source epidemic, therefore repeated peaks on the epidemic curve (rise of the daily number of the newly diagnosed infections) are to be anticipated. The errors between the predicted and validated data and trends seems to be low. Conclusions The influence of this pandemic is great worldwide, impact our everyday lifes. Especially decision makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based predictions might be useful tools for predictions and the models can be recalculated according to the new observed data, to get more precise forecast of the pandemic.

Introduction Using ultrasound to measure optic nerve sheath diameter (ONSD) has been shown to be a useful modality to detect elevated intracranial pressure. However, manual assessment of ONSD by a human operator is cumbersome and prone to human errors. We aimed to develop and test an automated algorithm for ONSD measurement using ultrasound images and compare it to measurements performed by physicians. Materials and Methods Patients were recruited from the Neurological Intensive Care Unit. Ultrasound images of the optic nerve sheath from both eyes were obtained using an ultrasound unit with an ocular preset. Images were processed by two attending physicians to calculate ONSD manually. The images were processed as well using a novel computerized algorithm that automatically analyzes ultrasound images and calculates ONSD. Algorithm-measured ONSD was compared with manually measured ONSD using multiple statistical measures. Results Forty-four patients with an average/Standard Deviation (SD) intracranial pressure of 14 (9.7) mmHg were recruited and tested (with a range between 1 and 57 mmHg). A t-test showed no statistical difference between the ONSD from left and right eyes (P > 0.05). Furthermore, a paired t-test showed no significant difference between the manually and algorithm-measured ONSD with a mean difference (SD) of 0.012 (0.046) cm (P > 0.05) and percentage error of difference of 6.43% (P = 0.15). Agreement between the two operators was highly correlated (interclass correlation coefficient = 0.8, P = 0.26). Bland–Altman analysis revealed mean difference (SD) of 0.012 (0.046) (P = 0.303) and limits of agreement between −0.1 and 0.08. Receiver Operator Curve analysis yielded an area under the curve of 0.965 (P Conclusion The automated image-analysis algorithm calculates ONSD reliably and with high precision when compared to measurements obtained by expert physicians. The algorithm may have a role in computer-aided decision support systems in acute brain injury.

Low back pain(LBP) is the number one cause of disability worldwide. One factor which might potentially contribute to ongoing pain is impaired spinal movement variability. It is uncertain how movement variability changes during trunk movements in the presence of LBP. In this protocol, we will systematically investigate and compare both the amount and structure of spinal movement variability during repeated trunk motions between people with and without LBP. The results will be reported in line with the PRISMA(Preferred Reporting Items for Systematic Review and Meta-Analysis). Searches will be conducted on CENTRAL, PubMed, MEDLINE, EMBASE, and CINAHL databases, along with a comprehensive review of grey literature and key journals.Three pairs of two independent reviewers will screen potential studies and two independent reviewers assess the risk of bias within studies which meet the inclusion criteria. The Newcastle-Ottawa risk of bias tool will be used to assess the quality of the data.

Text is one of the most crucial information element present in our world. We still have more books and writings than sound and image data. This predominance engages the need for engineers in the field of computer science to enhance the comprehension of text by machines, and the capacity to retrieve or create information out of text data. Semantics is the study of the relationship between the meaning of signs in a particular language. The understanding of the different meaning of textual elements present in a sentence gives us the capacity to interpret text and give it a logical meaning. We have to note that in the absence of a link between those different meanings, a complete sentence cannot be formed and no meaning can, therefore, be extracted. Our goal is to make possible for a machine to grasp those meanings along with the semantic relationships and establish a knowledge, and once this knowledge is established into semantic sequences, converting it into a visual representation such as an image could deeply improve our capacity of comprehension of text and help individuals having trouble understanding a text. To achieve this, we decided to first define a text parsing and summarization technique using word embeddings and engage into a text to image conversion using a GAN. The logic behind this implementation is to give the computer the ability to grasp semantic sequences and define sequences of illustrations based on the text provided.